Unevenness inspection system, unevenness inspection method, and unevenness inspection program

ABSTRACT

An unevenness inspection system includes an image pickup section configured to acquire a picked-up image of an inspection target, an image generation section configured to generate a color-unevenness inspection image and a luminance-unevenness inspection image, based on the picked-up image, a calculation section configured to use both of the color-unevenness inspection image and the luminance-unevenness inspection image to calculate an evaluation parameter, and an inspection section configured to use the calculated evaluation parameter to perform unevenness inspection. The image generation section generates the color-unevenness inspection image and the luminance-unevenness inspection image, based on a filter-processed color-component image and a filter-processed luminance-component image. The calculation section calculates the evaluation parameter in consideration of unevenness visibility with respect to both of color and luminance.

TECHNICAL FIELD

The present disclosure relates to an unevenness inspection system, anunevenness inspection method, and an unevenness inspection program inwhich unevenness inspection (color-unevenness inspection andluminance-unevenness inspection) in color picture and the like isperformed.

BACKGROUND ART

Previously, a color-unevenness inspection and luminance-unevennessinspection in a mass-production process for a display unit that uses acathode ray tube (CRT), a liquid crystal display (LCD), or the likecapable of displaying a color picture, has been mainly performed withuse of a sensory test based on a comparison with a boundary sample. Thistechnique is carried out such that a display screen of the display unitas an inspection target is directly viewed by human being and therefore,this is an inspection close to actual use and also a simple and easytechnique.

However, this technique relies largely upon the capabilities ofindividual inspectors, and thus quality of inspection varies dependingon factors such as variations among the individual inspectors and theinspector's degree of fatigue. Therefore, it is difficult to perform astable inspection.

Under the circumstances, there have been proposed some techniques ofobjective unevenness inspection without depending on the capability ofthe inspector (for example, PTLs 1 to 5 and NPLs 1 to 3).

CITATION LIST Patent Literature

-   PTL 1: Japanese Unexamined Patent Application Publication No.    H1-225296-   PTL 2: Japanese Unexamined Patent Application Publication No.    H10-2800-   PTL 3: Japanese Unexamined Patent Application Publication No.    2003-57146-   PTL 4: Japanese Unexamined Patent Application Publication No.    H10-96681-   PTL 5: Japanese Unexamined Patent Application Publication No.    2007-198850

Non-Patent Literature

-   NPL 1: SID06 DIGEST 31.1-   NPL 2: Information Display 2007 1 pp. 2-6-   NPL 3: Proc. IS&T and SID Ninth Color Imaging Conference, 2001: pp.    153-157

SUMMARY OF INVENTION

However, in such unevenness inspection, (in the color-unevennessinspection and the luminance-unevenness inspection), typically, furtherappropriate method is desired, and such method is desirably proposed.

In view of the foregoing, it is desirable to provide an unevennessinspection system, an unevenness inspection method, and an unevennessinspection program capable of performing an appropriate unevennessinspection.

An unevenness inspection system according to an embodiment of thedisclosure includes: an image pickup section configured to acquire apicked-up image of an inspection target; an image generation sectionconfigured to generate a color-unevenness inspection image and aluminance-unevenness inspection image, based on the picked-up image; acalculation section configured to use both of the color-unevennessinspection image and the luminance-unevenness inspection image tocalculate an evaluation parameter; and an inspection section configuredto use the calculated evaluation parameter to perform unevennessinspection. The image generation section performs image separationprocessing to separate a color component and a luminance component onthe picked-up image, to generate a color-component image and aluminance-component image, and individually performs filter processingtaking account of visual spatial frequency characteristics on thecolor-component image and the luminance-component image to respectivelygenerate the color-unevenness inspection image and theluminance-unevenness inspection image, based on the filter-processedcolor-component image and the filter-processed luminance-componentimage. The calculation section calculates the evaluation parameter inconsideration of unevenness visibility with respect to both of color andluminance.

An unevenness inspection method according to an embodiment of thedisclosure includes: a step of acquiring a picked-up image of aninspection target; a generation step of generating a color-unevennessinspection image and a luminance-unevenness inspection image, based onthe picked-up image; a calculation step of using both of thecolor-unevenness inspection image and the luminance-unevennessinspection image to calculate an evaluation parameter; and an inspectionstep of using the calculated evaluation parameter to perform unevennessinspection. In the generation step, image separation processing toseparate a color component and a luminance component is performed on thepicked-up image to generate a color-component image and aluminance-component image, and filter processing taking account ofvisual spatial frequency characteristics is individually performed onthe color-component image and the luminance-component image torespectively generate the color-unevenness inspection image and theluminance-unevenness inspection image, based on the filter-processedcolor-component image and the filter-processed unevenness-componentimage. In the calculation step, the evaluation parameter is calculatedin consideration of unevenness visibility with respect to both of colorand luminance.

An unevenness inspection program according to an embodiment of thedisclosure causes a computer to execute: a step of acquiring a picked-upimage of an inspection target; a generation step of generating acolor-unevenness inspection image and a luminance-unevenness inspectionimage, based on the picked-up image; a calculation step of using both ofthe color-unevenness inspection image and the luminance-unevennessinspection image to calculate an evaluation parameter; and an inspectionstep of using the calculated evaluation parameter to perform unevennessinspection. In the generation step, image separation processing toseparate a color component and a luminance component is performed on thepicked-up image to generate a color-component image and aluminance-component image, and filter processing taking account ofvisual spatial frequency characteristics is individually performed onthe color-component image and the luminance-component image torespectively generate the color-unevenness inspection image and theluminance-unevenness inspection image, based on the filter-processedcolor-component image and the filter-processed luminance-componentimage. In the calculation step, the evaluation parameter is calculatedin consideration of unevenness visibility with respect to both of colorand luminance.

In the unevenness inspection system, the unevenness inspection method,and the unevenness inspection program according to the respectiveembodiments of the disclosure, the color-unevenness inspection image andthe luminance-unevenness inspection image are generated based on thepicked-up image of the inspection target, the evaluation parameter iscalculated with use of both of the color-unevenness inspection image andthe luminance-unevenness inspection image, and the unevenness inspectionis performed with use of the evaluation parameter. Here, the evaluationparameter is calculated in consideration of unevenness visibility withrespect to both of color and luminance. As a result, as compared withthe case where the unevenness inspection is performed withoutconsidering such visibility, objective unevenness inspection (thecolor-unevenness inspection and the luminance-unevenness inspection)further matched with human sense is realized. Also, to generate thecolor-unevenness inspection image and the luminance-unevennessinspection image, the filter processing taking account of visual spatialfrequency characteristics is performed after the image separationprocessing for separating a color component and a luminance component isperformed on the picked-up image. As a result, unlike the case where theabove-described filter processing is performed without performing suchimage separation processing, it is possible to avoid occurrence of falsecolor-unevenness component and false luminance-unevenness component, andtherefore, more accurate unevenness inspection is realized.

According to the unevenness inspection system, the unevenness inspectionmethod, and the unevenness inspection program of the respectiveembodiments of the disclosure, when the evaluation parameter iscalculated with use of both of the color-unevenness inspection image andthe luminance-unevenness inspection image, the calculation is performedin consideration of unevenness visibility to both of color andluminance. Therefore, it is possible to realize objective unevennessinspection further matched with human sense. Moreover, when thecolor-unevenness inspection image and the luminance-unevennessinspection image are generated, the filter processing taking account ofvisual spatial frequency characteristics is performed after the imageseparation processing for separating a color component and a luminancecomponent is performed. Therefore, occurrence of false color-unevennesscomponent and false luminance-unevenness component is avoided, and it ispossible to realize more accurate unevenness inspection. Consequently,it becomes possible to perform appropriate unevenness inspection.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram illustrating an outline configurationexample of an unevenness inspection system according to an embodiment ofthe disclosure, together with a display unit as an inspection target.

FIG. 2 is a flowchart illustrating an example of unevenness inspectionprocessing performed in an image processing apparatus illustrated inFIG. 1.

FIG. 3 is a flowchart illustrating detail of steps in filter processingand generation of color-unevenness inspection images illustrated in FIG.2.

FIG. 4 is a characteristic diagram illustrating an example of a methodof calculating chroma illustrated in FIG. 2.

FIG. 5A is a characteristic diagram illustrating an example ofrelationship between an area ratio of color-unevenness region for eachcolor group and a subjective evaluation value of color-unevenness.

FIG. 5B is a characteristic diagram illustrating an example ofrelationship between maximum chroma in a color-unevenness region and asubjective evaluation value of color-unevenness.

FIG. 6A is a diagram illustrating an example of an image produced forcolor-unevenness inspection processing.

FIG. 6B is a diagram illustrating another example of the image producedfor the color-unevenness inspection processing.

FIG. 6C is a diagram illustrating still another example of the imageproduced for the color-unevenness inspection processing.

FIG. 6D is a diagram illustrating still another example of the imageproduced for the color-unevenness inspection processing.

FIG. 7 is a schematic diagram for explaining definition of a chroma edgeregion and a luminance edge region.

FIG. 8 is a flowchart illustrating detail of steps in filter processingand generation of luminance-unevenness inspection images illustrated inFIG. 2.

FIG. 9A is a diagram illustrating an example of an image produced forluminance-unevenness inspection processing.

FIG. 9B is a diagram illustrating another example of the image producedfor the luminance-unevenness inspection processing.

FIG. 9C is a diagram illustrating still another example of the imageproduced for the luminance-unevenness inspection processing.

FIG. 9D is a diagram illustrating still another example of the imageproduced for the luminance-unevenness inspection processing.

FIG. 10A is a characteristic diagram illustrating relationship betweenvarious kinds of subjective evaluation values and a color-unevennessevaluation value according to Example 1.

FIG. 10B is a characteristic diagram illustrating relationship betweenvarious kinds of subjective evaluation values and a luminance-unevennessevaluation value according to the Example 1.

FIG. 10C is a characteristic diagram illustrating relationship betweenvarious kinds of subjective evaluation values and an integratedevaluation value according to the Example 1.

FIG. 11A is a diagram for explaining an evaluation condition accordingto Example 2.

FIG. 11B is another diagram for explaining the evaluation conditionaccording to the Example 2.

FIG. 12 is a diagram illustrating luminance edge images according to theExample 2.

FIG. 13 is a diagram illustrating chroma edge images and binarizedcolor-unevenness images according to a comparative example and Example3.

FIG. 14 is a diagram illustrating luminance edge images and binarizedluminance-unevenness images according to the comparative example and theExample 3.

FIG. 15 is a diagram for explaining an effect in a case where avariation per unit visual angle is used as an edge threshold.

FIG. 16 is a schematic diagram illustrating an outline configurationexample of an unevenness inspection system according to a modification1, together with inspection targets.

FIG. 17 is a schematic diagram illustrating an outline configurationexample of an unevenness inspection system according to a modification2, together with inspection targets.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment of the disclosure is described in detail withreference to drawings. Note that the description will be given in thefollowing order.

1. Embodiment (an example of unevenness inspection in which filterprocessing is performed after image separation processing for separatinga color component and a luminance component)

2. Modifications 1 and 2 (a configuration example in which an imageprocessing function is provided in a server to perform networkconnection)

3. Other modifications

Embodiment

[Configuration]

FIG. 1 schematically illustrates an outline configuration example of anunevenness inspection system (an unevenness inspection system 1)according to an embodiment of the disclosure, together with a displayunit 4 serving as an inspection target. This unevenness inspectionsystem 1 performs integrated unevenness inspection includingcolor-unevenness inspection and luminance-unevenness inspection on acolor picture displayed on the display unit 4 or the like, and includesan image processing apparatus 2 and an image pickup apparatus 3 (animage pickup section). Here, as the display unit 4, for example, varioustypes of displays such as a CRT, an LCD, a plasma display panel (PDP),and an organic electro luminescence (EL) display may be applied.Incidentally, an unevenness inspection method and an unevennessinspection program according to respective embodiments of the disclosureare embodied in the unevenness inspection system 1 of the presentembodiment, and therefore they will be described together below.

(Image Pickup Apparatus 3)

The image pickup apparatus 3 is used to pick up an image of a displayscreen (a color display screen) of the display unit 4 that is aninspection target in the above-described unevenness inspection. Theimage pickup apparatus 3 may be configured using, for example, an imagepickup device such as a charge coupled device (CCD) and a complementarymetal oxide semiconductor (CMOS). A picked-up image (picked-up imagedata Din) obtained through the image-pickup by the image pickupapparatus 3 is output to the image processing apparatus 2 through aconnecting wire 10. Incidentally, FIG. 1 illustrates a case where theconnecting wire 10 is a cable, but the image pickup apparatus 3 and theimage processing apparatus 2 may be wirelessly connected to each other.

(Image Processing Apparatus 2)

The image processing apparatus 2 performs the unevenness inspectionbased on the picked-up image data Din output from the image pickupapparatus 3, and outputs inspection result data Dout as a result of theinspection. The image processing apparatus 2 may be configured using,for example, a personal computer (PC) as illustrated in the figure, orthe like. The image processing apparatus 2 includes an image generationsection 21, a parameter calculation section 22 (a calculation section),and an inspection processing section 23 (an inspection section).

The image generation section 21 performs predetermined image processingbased on the picked-up image data Din, thereby generatingcolor-unevenness inspection images and luminance unevenness inspectionimages that will be described later. Specifically, the image generationsection 21 generates, as the color-unevenness inspection images, acolor-unevenness image (color-unevenness image data D11), a chroma edgeimage (chroma edge image data D12), and a binarized color-unevennessimage (binarized color-unevenness image data D13) that are describedlater. Also, the image generation section 21 generates, as theluminance-unevenness inspection images, a luminance-unevenness image(luminance-unevenness image data D21), a luminance edge image (luminanceedge image data D22), and a binarized luminance-unevenness image(binarized luminance-unevenness image data D23) that are describedlater. To generate the color-unevenness inspection images and theluminance-unevenness inspection images in this way, the image generationsection 21 performs predetermined filter processing taking account ofvisual spatial frequency characteristics after performing imageseparation processing for separating color component and luminancecomponent described later. Moreover, the image generation section 21generates the above-described color-unevenness inspection images whileperforming correction processing (gain correction processing describedlater) taking account of difference of color-unevenness visibilitydepending on colors. Note that the detail of the image processing (theimage generation processing) by the image generation section 21 will bedescribed later.

The parameter calculation section 22 calculates various kinds ofevaluation parameters for the unevenness inspection described later withuse of both of the color-unevenness inspection images (theabove-described various kinds of image data D11 to D13) and theluminance-unevenness inspection images (the above-described variouskinds of image data D21 to D23) that are generated by the imagegeneration section 21. Specifically, the parameter calculation section23 calculates a color-unevenness evaluation value Ec (color-unevennessparameter) described later with use of the color unevenness inspectionimages (the various kinds of image data D11 to D13). Also, the parametercalculation section 22 calculates a luminance-unevenness evaluationvalue El (a luminance-unevenness parameter) described later with use ofthe luminance-unevenness inspection images (the various kinds of imagedata D21 to D23). Then, the parameter calculation section 22 weights andadds the color-unevenness evaluation value Ec and theluminance-unevenness evaluation value EI to calculate an integratedevaluation value E (an integrated evaluation parameter) as theabove-described evaluation parameter. At this time, in the presentembodiment, the parameter calculation section 22 calculates theintegrated evaluation value E in consideration of unevenness visibilitywith respect to both of color and luminance. Note that the detail of thecalculation processing by the parameter calculation section 22 will alsobe described later.

The inspection processing section 23 performs the unevenness inspection(the integrated unevenness inspection including the color-unevennessinspection and the luminance-unevenness inspection) of the displayscreen of the display unit 4 that is the inspection target, with use ofthe integrated evaluation value E calculated by the parametercalculation section 22. Thus, the inspection result data Dout as theresult of the inspection is output from the inspection processingsection 23. Note that the detail of the unevenness inspection processingby the inspection processing section 23 will also be described later.

[Action and Effects]

Subsequently, action and effects of the unevenness inspection system 1according to the present embodiment will be described.

(1. Basic Operation)

In the unevenness inspection system 1, when an image of the displayscreen of the display unit 4 that is the inspection target is picked upby the image pickup apparatus 3, a picked-up image (the picked-up imagedata Din) is obtained. The picked-up image data Din is input to theimage generation section 21 in the image processing apparatus 2 throughthe connecting wire 10.

The image generation section 21 performs the predetermined imageprocessing based on the picked-up image data Din to generate thecolor-unevenness inspection images (the various kinds of image data D11to D13) and the luminance-unevenness inspection images (the variouskinds of image data D21 to D23). Then, the parameter calculation section22 uses both of the color-unevenness inspection images and theluminance-unevenness inspection images to calculate the integratedevaluation value E that is the evaluation parameter for the unevennessinspection. Then, the inspection processing section 23 uses theintegrated evaluation value E to perform the unevenness inspection onthe display screen of the display unit 4 that is the inspection target.As a result, the inspection result data Dout as the inspection result isoutput from the inspection processing section 23.

(2. Detail of Unevenness Inspection Processing)

Next, there will be described the detail of the unevenness inspectionprocessing by the image processing apparatus 2 in the unevennessinspection system 1 according to the present embodiment. FIG. 2 is aflowchart illustrating an example of the unevenness inspectionprocessing performed in the image processing apparatus 2.

(2-1. Preprocessing)

First, as described above, the image generation section 21 obtains thepicked-up image (the picked-up image data Din) of the inspection targetfrom the image pickup apparatus 3 through the connecting wire 10 (stepS101 in FIG. 2).

Subsequently, the image generation section 21 converts a signal of thepicked-up image data Din into a signal (Xi, Yi, Zi) formed oftristimulus values X, Y, and Z (step S102). Specifically, for example,when the picked-up image data Din is a picture signal in sRGB standard,the image generation section 21 performs conversion with use of thefollowing expression (1). Further, when the picked-up image data Din isa picture signal in other standard, the image generation section 21similarly performs conversion in accordance with such a standard togenerate the signal (Xi, Yi, Zi). Incidentally, although the case wherethe signal of the picked-up image data Din is converted into the signal(Xi, Yi, Zi) is described here, the signal (Xi, Yi, Zi) may be directlyobtained by the image pickup apparatus 3.

[Numerical Expression 1]

(when picked-up image data Din is in sRGB standard (based on IEC61966-2-1))

$\begin{matrix}{\begin{bmatrix}X_{i} \\Y_{i} \\Z_{i}\end{bmatrix} = {\begin{bmatrix}0.4124 & 0.3576 & 0.1805 \\0.2126 & 0.7152 & 0.0722 \\0.0193 & 0.1192 & 0.9505\end{bmatrix}\begin{bmatrix}R_{sRGB} \\G_{sRGB} \\B_{sRGB}\end{bmatrix}}} & (1)\end{matrix}$

Subsequently, the image generation section 21 performs predeterminednoise removal processing as preprocessing on the signal (Xi, Yi, Zi)(step S103). Specifically, for example, the image generation section 21may use a spatial filter such as Median Filter to perform processing ofremoving noise caused by a kind of the image pickup apparatus 3 and theimage pickup condition. Incidentally, depending on a case, such noiseremoval processing may not be performed. Further, when the picked-upimage data Din is a picture signal in sRGB standard, the imagegeneration section 21 may perform the noise removal processing directlyon the picked-up image data Din.

(Image Separation Processing)

Then, the image generation section 21 performs image separationprocessing for separating a color component and a luminance componentdescribed later on the signal (Xi, Yi, Zi) that has been subjected tothe noise removal processing, to generate a color-component image(color-component image data D10) and a luminance-component image(luminance-component image data D20) (step S104).

Specifically, the image generation section 21 generates thecolor-component image data D10 in the following manner, based on thesignal (Xi, Yi, Zi) that has been subjected to the noise removalprocessing. More specifically, the image generation section 21 removesluminance distribution information from the signal (Xi, Yi, Zi) that hasbeen subjected to the noise removal processing, while maintaining colordistribution information, to generate the color-component image data D10that is formed of a signal (XC, YC, ZC). At this time, to remove theluminance distribution information, an average value or a most frequentvalue of Yi is added to all of the image pickup pixels (display pixels)to calculate YC. Incidentally, the value to be added is not limited tothe average value or the most frequent value of Yi, and may be aconstant value. Moreover, to maintain the color distributioninformation, the above-described YC is used to calculate XC and ZC sothat the values (a*, b*) calculated from (X, Y, Z) signal by using thefollowing expression (2) are not changed.

Here, the values (a*, b*) are values in the CIE 1976 L*a*b* color space(CIELAB color space) recommended by the Commission International del'Éclairage (CIE) in 1976. The CIELAB color space is recommended as auniform color space and is a space in consideration of uniformity withrespect to human's visual perception of colors. Further, Xn, Yn and Znin the expression (2) are tristimulus values of a perfect reflectingdiffuser.

$\begin{matrix}\left\lbrack {{Numerical}\mspace{14mu}{Expression}\mspace{14mu} 2} \right\rbrack & \; \\\left\{ {{\begin{matrix}{L^{*} = {{116{f\left( {Y/Y_{n}} \right)}} - 16}} \\{a^{*} = {500\left\lbrack {{f\left( {X/X_{n}} \right)} - {f\left( {Y/Y_{n}} \right)}} \right\rbrack}} \\{b^{*} = {200\left\lbrack {{f\left( {Y/Y_{n}} \right)} - {f\left( {Z/Z_{n}} \right)}} \right\rbrack}}\end{matrix}{where}},{{f(t)} = \left\{ \begin{matrix}t^{1/3} & \left( {t > \left( {6/29} \right)^{3}} \right) \\{{\frac{1}{3}\left( \frac{29}{6} \right)^{3}t} + \frac{4}{29}} & ({otherwise})\end{matrix} \right.}} \right. & (2)\end{matrix}$

More specifically, the image generation section 21 uses the followingexpressions (3) and (4) to calculate XC and ZC.500×[f(Xi/Xn)−f(Yi/Yn)]=500×[f(XC/Xn)−f(YC/Yn)]  (3)

-   -   so that a*(Xi,Yi,Zi)=a*(XC,YC,ZC)        200×[f(Yi/Yn)−f(Zi/Zn)]=200×[f(YC/Yn)−f(ZC/Zn)]  (4)    -   so that b*(Xi, Yi, Zi)=b*(XC,YC,ZC)

On the other hand, the image generation section 21 generates theluminance-component image data D20 in the following manner, based on thesignal (Xi, Yi, Zi) that has been subjected to the noise removalprocessing. Specifically, the image generation section 21 removes thecolor distribution information from the signal (Xi, Yi, Zi) that hasbeen subjected to the noise removal processing, while maintaining theluminance distribution information, to generate the luminance-componentimage data D20 that is formed of a signal (XL, YL, ZL). At this time, tomaintain the luminance distribution information, the value of Yi isadded as is to all of the image pickup pixels (display pixels) tocalculate YL. Further, to remove the color distribution information, theabove-described YL is used to calculate XL and ZL so that theabove-described values (a*, b*) become 0 (zero).

More specifically, the image generation section 21 uses the followingexpressions (5) and (6) to calculate XL and ZL.500×[f(XL/Xn)−f(YL/Yn)]=0  (5)

-   -   so that a*(XL, YL, ZL)=0        200×[f(YL/Yn)−f(ZL/Zn)]=0  (6)    -   so that b*(XL,YL,ZL)=0

(2-2. Filter Processing and Generation of Color-Unevenness InspectionImage)

Subsequently, the image generation section 21 performs predeterminedfilter processing on the color-component image (the color-componentimage data D10) thus generated, and generates the color-unevennessinspection images (the various kinds of image data D11 to D13) based onthe filter-processed color-component image data D10 (step S11).

FIG. 3 is a flowchart illustrating the detail of the steps (steps S111to S119) in the filter processing and the generation of thecolor-unevenness inspection images.

(Filter Processing)

At this step, first, the image generation section 21 performs (w/k, r/g,b/y) conversion that is defined by the following expression (7), on thecolor-component image data D10 formed of the signal (XC, YC, ZC) (stepS111 in FIG. 3). As a result, the color-component image data D10 isconverted from (X, Y, Z) coordinate system to (w/k, r/g, b/y) coordinatesystem. Then, 2D Fourier transform for every three axes (components) isperformed on coordinate-converted (w/k, r/g, b/y) signal to develop thecoordinate-converted color-component image data D10 to spatialfrequency. Note that (w/k) indicates (white/black), (r/g) indicates(red/green), and (b/y) indicates (blue/yellow).

$\begin{matrix}\left\lbrack {{Numerical}\mspace{14mu}{Expression}\mspace{14mu} 3} \right\rbrack & \; \\{\begin{bmatrix}{w/k} \\{r/g} \\{b/y}\end{bmatrix} = {\begin{bmatrix}0.279 & 0.720 & {- 0.107} \\{- 0.449} & 0.290 & {- 0.077} \\0.086 & {- 0.590} & 0.501\end{bmatrix}\begin{bmatrix}X \\Y \\Z\end{bmatrix}}} & (7)\end{matrix}$

Subsequently, the image generation section 21 performs filter processingtaking account of visual spatial frequency characteristics (contrastsensitivity factor) on the 2D-Fourier-transformed data (step S112).Here, the visual spatial frequency characteristics are determined bysubjective evaluation experiment, and are defined by opposite colorspace by human object recognition and corresponds to three axes of (w/k,r/g, b/y). Performing filter processing taking account of such visualspatial frequency characteristics makes it possible to performprocessing to allow the image to be close to human sensitivity. Notethat, after the filter processing, 2D inverse Fourier transform isperformed to return the filter-processed data D10 to (w/k, r/g, b/y)coordinate system.

Next, the image generation section 21 performs (X, Y, Z) conversiondefined by the following expression (8), on the filter-processed (w/k,r/g, b/y) signal (step S113). As a result, the coordinate conversionfrom (w/k, r/g, b/y) coordinate system to (X, Y, Z) coordinate system isperformed.

$\begin{matrix}{\mspace{20mu}\left\lbrack {{Numerical}\mspace{14mu}{Expression}\mspace{14mu} 4} \right\rbrack} & \; \\{\begin{bmatrix}X \\Y \\Z\end{bmatrix} = {\begin{bmatrix}0.626554504 & {- 1.867177598} & {- 0.153156373} \\1.36985545 & 0.934755824 & 0.436229005 \\1.505650755 & 1.421323772 & 2.53602108\end{bmatrix}\begin{bmatrix}{w/k} \\{r/g} \\{b/y}\end{bmatrix}}} & (8)\end{matrix}$

In this way, in the present embodiment, after the image separationprocessing for separating the color component and the luminancecomponent is performed on the picked-up image data Din, the filterprocessing taking account of visual spatial frequency characteristics isperformed. As a result, unlike the case where the above-described filterprocessing is performed without such image separation processing, it ispossible to avoid occurrence of false color-unevenness component, and torealize more accurate unevenness inspection.

The reason why the such false color-unevenness component occurs is asfollows. Specifically, in the spatial frequency characteristics in theabove-described filter processing, (w/k) that is an axis of theluminance component and (r/g) and (b/y) that are axes of the colorcomponent are largely different in low frequency characteristics fromeach other. Further, as illustrated in the above-described expression(2), L* is basically calculated from only the value Y, whereas the valueY is also used in calculation of the values (a*, b*). Accordingly, ifthe value Y that is largely influenced by (w/k) subjected to the filterprocessing that is largely different in visual spatial frequencycharacteristics is used, color distribution information that does notordinary exist is generated, and false color-unevenness easily occurs inthe image. Accordingly, in the present embodiment, the filter processingtaking account of visual spatial frequency characteristics is performedafter the image separation processing is performed as described above,to avoid occurrence of false color-unevenness component caused bydifference between the spatial frequency characteristics (low frequencycharacteristics) of the color component and the spatial frequencycharacteristics of the luminance component.

Subsequently, the image generation section 21 uses the above-describedexpression (2) to calculate the above-described values (a*, b*), basedon the signal (X, Y, Z) that is obtained through the above-described (X,Y, Z) conversion (step S114).

Next, the image generation section 21 generates the above-describedvarious kinds of color-unevenness inspection images while performingcorrection processing (the gain correction processing) taking account ofdifference of color-unevenness visibility depending on colors, in eachof the image pickup pixels. Specifically, the image generation section21 calculates chroma C while performing such correction processing ineach of the image pickup pixels. More specifically, first, the imagegeneration section 21 performs the gain correction processing (thecorrection processing using gain α) represented by the followingexpression (9) as the correction processing taking account of differenceof the color-unevenness visibility on the value a* calculated at thestep S114 (step S115). Then, the image generation section 21 uses thevalues (a*′, b*) calculated at the steps S114 and S115 to calculate thechroma C for each of the image pickup pixels by the following expression(10) (step S116).

$\begin{matrix}{{a^{*^{\prime}} = \left( {\alpha \times a^{*}} \right)}\left( {{{{For}\mspace{14mu} a^{*}} > {0\text{:}{gain}\mspace{14mu}\alpha} > 1},{{{{for}\mspace{14mu} a^{*}} \leq {0\text{:}{gain}\mspace{14mu}\alpha}} = 1}} \right.} & (9) \\\begin{matrix}{C = \left\{ {\left( a^{*^{\prime}} \right) + \left( b^{*} \right)^{2}} \right\}^{1/2}} \\{= \left\{ {\left( {\alpha \times a^{*}} \right)^{2} + \left( b^{*} \right)^{2}} \right\}^{1/2}}\end{matrix} & (10)\end{matrix}$

Such gain correction processing corresponds to conversion (correction)of a point of (a*, b*)=(a1, b1) into a point of (a*, b*)=(α×a1, b1),considering (a*, b*) coordinate system as illustrated in FIG. 4.Accordingly, a curve illustrating the chroma C before and after the gaincorrection processing is as illustrated in FIG. 4. Specifically, thecurve illustrating the chroma C before the gain correction processing iscircular, whereas the curve illustrating the chroma C after the gaincorrection processing is not circular but ellipsoidal in a region ofa*>0 as illustrated by an arrow in FIG. 4.

Here, the reason why the chroma C is calculated after such gaincorrection processing is performed is as follows. Specifically, this isbecause visibility of color-unevenness (color-unevenness visibility)perceived by human being varies depending on a kind of color configuringthe color-unevenness.

More specifically, first, the color-unevenness visibility (ME value,subjective evaluation value of unevenness (here, color-unevenness) byhuman being) varies depending on an area ratio of color-unevennessregion for each color group (an area ratio of color-unevenness regionfor each color group with respect to entire region of the inspectiontarget (entire display pixel region in the display screen)). In otherwords, for example, as illustrated in FIG. 5A, in the area ratio of acolor group corresponding to colors of red (R), orange (R), and magenta(M), the ME value (the color-unevenness visibility) at a certain arearatio becomes higher as compared with the area ratio of a color groupcorresponding to colors of yellow green (YG), green (G), and light blue(LB).

Further, the color-unevenness visibility (the ME value) varies dependingon a color group to which the color exhibiting a maximum chroma Cmax(maximum chroma in the entire color-unevenness region) described laterbelongs. In other words, for example, as illustrated in FIG. 5B, when acolor belonging to a color group corresponding to colors of red (R),orange (O), and magenta (M) exhibits the maximum chroma Cmax, the MEvalue (the color-unevenness visibility) at the same maximum chroma Cmaxbecomes higher as compared with the case where a color belonging to acolor group corresponding to colors of yellow green (YG), green (G), andlight blue (LB) exhibits the maximum chroma Cmax.

Therefore, in the present embodiment, the image generation section 21calculates the chroma C while performing the gain correction processingtaking account of difference of color-unevenness visibility depending oncolors as described above. Specifically, the image generation section 21performs the correction (the gain correction) to selectively increasethe value a*, on the region of a*>0 that corresponds to the color groupwhose color-unevenness visibility is relatively high (the color groupcorresponding to colors of red (R), orange (O), and magenta (M)). As aresult, as compared with the case where the unevenness inspection (thecolor-unevenness inspection) is performed without taking account of thedifference of the color-unevenness visibility depending on colors,objective unevenness inspection further matched with human sense isrealized.

(Generation of Color-Unevenness Inspection Image)

Next, the image generation section 21 uses the chroma C thus calculatedto generate a color-unevenness image (the color-unevenness image dataD11) that is one of the color-unevenness inspection images, from thepicked-up image (step S117). Specifically, the image generation section21 generates the color-unevenness image configured of the value of thechroma C for each of the image pickup pixels. As a result, for example,the color-unevenness image configured of the color-unevenness image dataD11 as illustrated in FIG. 6A may be generated.

Then, the image generation section 21 also uses the calculated chroma Cto generate the chroma edge image (the chroma edge image data D12) thatis one of the color-unevenness inspection images, from the picked-upimage (step S118). Specifically, for example, the image generationsection 21 may perform Sobel filter processing or the like to identifythe chroma edge region, and accordingly generates the chroma edge image.As a result, the chroma edge image configured of the chroma edge imagedata D12 as illustrated in FIG. 6B may be generated.

Here, the chroma edge region identified at this time may be defined as,for example, a region in which chroma variation (chroma edge intensity)per unit length in the inspection target (the display screen) or chromavariation per unit visual angle is equal to or greater than apredetermined threshold (a chroma edge threshold). Specifically, forexample, as illustrated in (A) of FIG. 7, a region (for example, aregion Ae in (A) of FIG. 7) whose chroma variation per unit length isequal to or greater than the chroma edge threshold (for example,(dC*/mm)=2.0) is identified as the chroma edge region. This chroma edgethreshold is defined with respect to unit length on a display screen 40so as to match with sensitivity of color-unevenness perceived by humanbeing. Alternatively, for example, as illustrated in (B) of FIG. 7, aregion (for example, the region Ae in (B) of FIG. 7) whose chromavariation per unit visual angle is equal to or greater than the chromaedge threshold (for example, (dC*/arcmin)=0.873) is identified as thechroma edge region. This chroma edge threshold is defined with respectto unit visual angle θ by an observer (an eye Ey) so as to match withsensitivity of color-unevenness perceived by human being. Note that, asthe visual angle θ at this time, for example, the visual angle definedin the following manner may be desirably used. Specifically, when theeyesight of human being is 1.0, resolution of the angle identified byhuman being is defined as one arc-minute that is one-sixtieth of onedegree. Therefore, the visual angle θ defined with use of one arc-minutein consideration of such visual characteristics of human being may bedesirably used. The same applies to the following description, howeverthe definition is not limited thereto.

Subsequently, further, the image generation section 21 uses thegenerated color-unevenness image (the color-unevenness image data D11)to generate the binarized color-unevenness image (the binarizedcolor-unevenness image data D13), and identifies the color-unevennessregion (step S119). At this time, the image generation section 21identifies the color-unevenness region based on the intensity of thechroma C in each of the image pickup pixels. Specifically, the imagepickup pixel in which the value of the chroma C is equal to or greaterthan the predetermined threshold (for example, 2.0) is identified as theimage pickup pixel belonging to the color-unevenness region. On theother hand, the image pickup pixel in which the value of the chroma C issmaller than the above-described threshold is identified as the imagepickup pixel not belonging to the color-unevenness region. Thus, theimage generation section 21 identifies the color-unevenness region. As aresult, for example, as the binarized color-unevenness image (thebinarized color-unevenness image data D13) illustrated in FIG. 6C, thecolor-unevenness region may be identified. Note that, in the binarizedcolor-unevenness image illustrated in FIG. 6C, the color-unevennessregion is illustrated by red, and other regions are illustrated by black(the image illustrated in FIG. 6C is the binarized image).

(2-3. Calculation of Color-Unevenness Evaluation Value Ec)

Subsequently, the parameter calculation section 22 calculates thecolor-unevenness evaluation value Ec in the following manner (steps S121to S122 in FIG. 2).

First, the parameter calculation section 22 uses the various kinds ofcolor-unevenness inspection images (the color-unevenness image data D11,the chroma edge image data D12, and the binarized color-unevenness imagedata D13) generated in the above manner, to calculate various kinds ofparameters described below (step S121).

Specifically, the parameter calculation section 22 uses the chroma edgeimage (the chroma edge image data D12) to calculate a chroma edge arearatio Sce that is an area ratio of the chroma edge region with respectto the entire region of the inspection target (the entire display pixelregion in the display screen).

Further, the parameter calculation section 22 uses the binarizedcolor-unevenness image (the binarized color-unevenness image data D13)to calculate a color-unevenness area ratio Sc that is an area ratio ofthe color-unevenness region with respect to the entire region of theinspection target (the entire display pixel region in the displayscreen).

Furthermore, the parameter calculation section 22 uses thecolor-unevenness image (the color-unevenness image data D11) tocalculate the maximum chroma Cmax in the entire color-uneven region. Forexample, in the example of the color-unevenness image illustrated inFIG. 6A, the maximum chroma Cmax is exhibited in the image pickup pixelindicated by a symbol “x” in FIG. 6D.

Then, the parameter calculation section 22 weights and adds the chromaedge area ratio Sce, the color-unevenness area ratio Sc, and the maximumchroma Cmax that are thus calculated, to calculate the color-unevennessevaluation value Ec (step S122). Specifically, the parameter calculationsection 22 may use, for example, the following expression (11) tocalculate the color-unevenness evaluation value Ec. Note that, in theexpression (11), each of constants (coefficients) k1, k2, and k3represents a weighting coefficient, and c1 represents a predeterminedconstant (including 0 (zero)).Ec=k1×Sce+k2×Sc+k3×Cmax+c1  (11)

(2-4. Filter Processing and Generation of Luminance-UnevennessInspection Image)

Also, the image generation section 21 performs the following processingon the luminance-component image (the luminance-component image dataD20) generated at the above-described step S104 (in the image separationprocessing). Specifically, the image generation section 21 performs theabove-described predetermined filter processing, and generates theluminance-unevenness inspection images (the various kinds of image dataD21 to D23) based on the filter-processed luminance-component image dataD20 (step S13).

FIG. 8 is a flowchart illustrating the detail of steps (steps S131 toS138) in the filter processing and the generation of theluminance-unevenness inspection images.

(Filter Processing)

At this step, first, the image generation section 21 performs (w/k, r/g,b/y) conversion defined by the above-described expression (7), on theluminance-component image data D20 formed of the signal (XL, YL, ZL)(step S131 in FIG. 8). As a result, the luminance-component image dataD20 is converted from (X, Y, Z) coordinate system to (w/k, r/g, b/y)coordinate system. Then, 2D Fourier transform for every three axes isperformed on the coordinate-converted (w/k, r/g, b/y) signal to developthe coordinate-converted luminance-component image data D20 to spatialfrequency.

Then, the image generation section 21 performs the filter processingtaking account of visual spatial frequency characteristics on the2D-Fourier-transformed data, in a manner similar to the above-describedstep S112 (step S132). Note that, after the filter processing, 2Dinverse Fourier transform is performed to return the data to (w/k, r/g,b/y) coordinate system.

Next, the image generation section 21 performs (X, Y, Z) conversiondefined by the above-described expression (8), on the filter-processedsignal (w/k, r/g, b/y) (step S133). As a result, the filter-processed(w/k, r/g, b/y) signal is converted from (w/k, r/g, b/y) coordinatesystem to (X, Y, Z) coordinate system.

Also here, in the present embodiment, after the image separationprocessing for separating the color component and the luminancecomponent is performed on the picked-up image data Din, the filterprocessing taking account of the visual spatial frequencycharacteristics is performed. Accordingly, unlike the case where theabove-described filter processing is performed without performing suchimage separation processing, occurrence of false luminance-unevennesscomponent caused by difference of the spatial frequency characteristics(the low frequency characteristics) between the color component and theluminance component is avoided, which makes it possible to realize moreaccurate unevenness inspection.

Then, the image generation section 21 calculates L* (luminosity), basedon the signal (X, Y, Z) obtained through the above-described (X, Y, Z)conversion (step S134). Specifically, the image generation section 21uses the above-described expression (2) to calculate the luminance L*for each of the image pickup pixels.

Subsequently, the image generation section 21 calculates averageluminance L*ave that is an average value of the luminance L* in theentire region of the white image (in this case, the entire display pixelregion of the white image displayed on the display screen of the displayunit 4) (step S135).

(Generation of Luminance-Unevenness Inspection Image)

Then, the image generation section 21 uses the luminance L* and theaverage luminance L*ave that have been calculated in this way, togenerate the luminance-unevenness image (the uneven-luminance image dataD21) that is one of the luminance-unevenness inspection images from thepicked-up image (step S136). Specifically, the image generation section21 calculates luminance difference ΔL* (=|L*−L*ave|) that is an absolutevalue of a difference between the luminance L* for each of the imagepickup pixels and the average luminance L*ave, and generatesluminance-unevenness image that is formed of the luminance differenceΔL*. As a result, the luminance-unevenness image formed of theluminance-unevenness image data D21, for example, as illustrated in FIG.9A may be generated. Incidentally, at this time, theluminance-unevenness image may be generated with use of the value of theluminance L* instead of the luminance difference ΔL* as described above.

Subsequently, the image generation section 21 also uses the calculatedluminance L* to generate the luminance edge image (the luminance edgeimage data D22) that is one of the luminance-unevenness inspectionimages, from the picked-up image (step S137). For example, the imagegeneration section 21 may perform, for example, Sobel filter processingor the like to identify the luminance edge region, and then generatesthe luminance edge image. As a result, the luminance edge image formedof the luminance edge image data D22, for example, as illustrated inFIG. 9B may be generated.

Here, the luminance edge region identified at this time may be defined,for example, as a region in which luminance variation (luminance edgeintensity) per unit length in the inspection target (the display screen)or luminance variation per unit visual angle is equal to or greater thana predetermined threshold (a luminance edge threshold). Specifically,also here, for example, as illustrated in (A) of FIG. 7, a region (forexample, the region Ae in (A) of FIG. 7) whose luminance variation perunit length is equal to or greater than the luminance edge threshold(for example, (dL*/mm)=0.5) is identified as the luminance edge region.This luminance edge threshold is defined with respect to unit length onthe display screen 40. Alternatively, for example, as illustrated in (B)of FIG. 7, a region (for example, the region Ae in (B) of FIG. 7) whoseluminance variation per unit visual angle is equal to or greater thanthe luminance edge threshold (for example, (dL*/arcmin)=0.218) isidentified as the luminance edge region. This luminance edge thresholdis defined with respect to unit visual angle θ by an observer (an eyeEy)

Subsequently, the image generation section 21 further uses the generatedluminance-unevenness image (the luminance-unevenness image data D21) togenerate the binarized luminance-unevenness image (the binarizedluminance-unevenness image data D23), and then identifies theluminance-unevenness region (a bright and dark region) (step S138). Atthis time, the image generation section 21 identifies theluminance-unevenness region based on level of the luminance differenceΔL* in each of the image pickup pixels. Specifically, the image pickuppixel in which the value of the luminance difference ΔL* is equal to orgreater than the predetermined threshold (for example, 0.3) isidentified as the image pickup pixel belonging to theluminance-unevenness region. On the other hand, the image pickup pixelin which the value of the luminance difference ΔL* is smaller than theabove-described threshold is identified as the image pickup pixel notbelonging to the luminance-unevenness region. Thus, the image generationsection 21 identifies the luminance-unevenness region. As a result, forexample, as the binarized luminance-unevenness image (the binarizedluminance-unevenness image data D23) illustrated in FIG. 9C, theluminance-unevenness region may be identified. Note that, in thebinarized luminance-unevenness image illustrated in FIG. 9C, theluminance-unevenness region is illustrated by white, and other regionsare illustrated by black (the image illustrated in FIG. 9C is thebinarized image).

(2-5. Calculation of Luminance-Unevenness Evaluation Value El)

Subsequently, the parameter calculation section 22 calculates theluminance-unevenness evaluation value El in the following manner (stepsS141 to S142 in FIG. 2).

First, the parameter calculation section 22 uses the various kinds ofluminance-unevenness inspection images (the luminance-unevenness imagedata D21, the luminance edge image data D22, and the binarizedluminance-unevenness image data D23) generated in the above manner, tocalculate various kinds of parameters described below (step S141).

Specifically, the parameter calculation section 22 uses the luminanceedge image (the luminance edge image data D22) to calculate a luminanceedge area ratio Sle that is an area ratio of the luminance edge regionwith respect to the entire region of the inspection target (the entiredisplay pixel region in the display screen).

Further, the parameter calculation section 22 uses the binarizedluminance-unevenness image (the binarized luminance-unevenness imagedata D23) to calculate a luminance-unevenness area ratio Sl that is anarea ratio of the luminance-unevenness region with respect to the entireregion of the inspection target (the entire display pixel region in thedisplay screen).

Furthermore, the parameter calculation section 22 uses theluminance-unevenness image (the luminance-unevenness image data D21) tocalculate a maximum luminance difference ΔL*max (=Max|L*−L*ave|) that isa maximum value of an absolute value of a difference between theluminance (L*) in the entire luminance-unevenness region and the averageluminance L*ave. For example, in the example of the luminance-unevennessimage illustrated in FIG. 9A, the maximum luminance difference ΔL*max isexhibited in the image pickup pixel indicated by a symbol “x” in FIG.9D.

Then, the parameter calculation section 22 weights and adds theluminance edge area ratio Sle, the luminance-unevenness area ratio Sl,and the maximum luminance difference ΔL*max that are thus calculated, tocalculate the luminance-unevenness evaluation value El (step S142).Specifically, the parameter calculation section 22 may use, for example,the following expression (12) to calculate the luminance-unevennessevaluation value El. Note that, in the expression (12), each ofconstants (coefficients) k4, k5, and k6 represents a weightingcoefficient, and c2 represents a predetermined constant (including 0).El=k4×Sle+k5×Sl+k6×ΔL*max+c2  (12)

(2-6. Calculation of Integrated Evaluation Value E and UnevennessInspection Processing)

Next, the parameter calculation section 22 may use, for example, thefollowing expression (13) to calculate the integrated evaluation value Efor the unevenness inspection, based on the color-unevenness evaluationvalue Ec and the luminance-unevenness evaluation value El that areobtained in this way (step S151). Specifically, the parametercalculation section 22 weights and adds the color-unevenness evaluationvalue Ec and the luminance-unevenness evaluation value El to calculatethe integrated evaluation value E. As a result, it becomes possible toperform inspection reflecting the weighting of the color-unevennessevaluation value Ec and the luminance-unevenness evaluation value El inthe unevenness inspection described below. Incidentally, in theexpression (13), each of constants (coefficients) A and B represents aweighting coefficient, and c3 represents a predetermined constant(including 0).E=A×Ec+B×El+c3  (13)

Here, in the present embodiment, the parameter calculation section 22calculates the integrated evaluation value E in consideration ofunevenness visibility with respect to both of color and luminance.Specifically, each of the above-described weighting coefficients A and Bis determined in consideration of the unevenness visibility with respectto both of color and luminance. In this way, since the integratedevaluation value E is calculated in consideration of the unevennessvisibility with respect to both of color and luminance, objectiveunevenness inspection further matched with human sense is realized, ascompared with the case where the unevenness inspection is performedwithout considering such visibility.

Subsequently, the inspection processing section 23 uses the integratedevaluation value E thus obtained, to perform unevenness inspection onthe display screen of the display unit 4 that is an inspection target,and then generates the inspection result data Dout as a result of theinspection (step S152). Specifically, for example, the inspectionprocessing section 23 determines that the degree of unevenness in theinspection target (one or both of the color-unevenness and theluminance-unevenness) is large, based on increase of the integratedevaluation value E. On the other hand, the inspection processing section23 determines that the degree of unevenness in the inspection target issmall, based on decrease of the integrated evaluation value E.Alternatively, when the integrated evaluation value E is equal to orgreater than the predetermined threshold, the inspection processingsection 23 determines that the inspection target is a defective, whereaswhen the integrated evaluation value E is smaller than theabove-described threshold, the inspection processing section 23determines that the inspection target is a confirming item. In this way,the unevenness inspection processing by the image processing apparatus 2is ended.

Example 1

Here, FIG. 10 illustrates an example (Example 1) illustratingrelationship (correlation) between the various kinds of evaluationvalues described above and the subjective evaluation value (the MEvalue). Specifically, FIG. 10A illustrates correlation between thecolor-unevenness evaluation value Ec and the subjective evaluation value(the ME value) according to the Example 1, FIG. 10B illustratescorrelation between the luminance-unevenness evaluation value El and thesubjective evaluation value (the ME value) according to the Example 1,and FIG. 10C illustrates correlation between the integrated evaluationvalue E and the subjective evaluation value (the ME value) according tothe Example 1. Note that a determination coefficient IV in a linear lineillustrated in these figures indicates that accuracy of the unevennessinspection becomes higher as the determination coefficient IV becomes alarge value close to “1”.

First, in an example illustrated in FIG. 10A, subjective evaluation wasperformed based on the evaluation result by magnitude estimation withrespect to 25 men and women between the ages of 19 and 24 as examinees.Moreover, in this example, the color-unevenness evaluation value Ec wascalculated when the weighting coefficient k1 with respect to the chromaedge area ratio Sce was 12.8, the weighting coefficient k2 with respectto the color-unevenness area ratio Sc was 4.0, and the weightingcoefficient k3 with respect to the maximum chroma Cmax was 0.02. In thisexample, the determination coefficient R2 was 0.94, which exhibitedextremely higher correlation.

On the other hand, an example illustrated in FIG. 10B was based on theevaluation result by the magnitude estimation under the conditionsimilar to that in the case of FIG. 10A. Moreover, in this example, theluminance-unevenness evaluation value El was calculated when theweighting coefficient k4 with respect to the luminance edge area ratioSle was 19.9, the weighting coefficient k5 with respect to theluminance-unevenness area ratio SI was 1.9, and the weightingcoefficient k6 with respect to the maximum luminance difference L*maxwas 0.19. Also in this example, the determination coefficient R2 was0.94, which exhibited extremely higher correlation.

On the other hand, an example illustrated in FIG. 10C was based on theevaluation result by the magnitude estimation under the conditionsimilar to that in the case of FIG. 10A. Moreover, in this example, theintegrated evaluation value E was calculated when the weightingcoefficient A with respect to the color-unevenness evaluation value Ecwas 0.63, and the weighting coefficient B with respect to theluminance-unevenness evaluation value El was 0.71. Also in this example,the determination coefficient R2 was 0.95, which exhibited extremelyhigher correlation.

Example 2

Also, FIG. 11A, FIG. 11B, and FIG. 12 each illustrate an example(Example 2) illustrating comparison of difference of the edge region atthe time when the edge region (the luminance edge region) was identifiedby comparison between the predetermined edge threshold and one of theabove-described variation per unit length and the above-describedvariation per unit visual angle.

Specifically, FIG. 11A illustrates relationship between the size [inch]of the display screen as the inspection target and appropriate visualdistance [mm] and the visual angle [ ] per 1 mm of the observer in eachsize (8 inches, 40 inches, and 80 inches) according to the Example 2.Also, FIG. 11B schematically illustrates relationship between eachappropriate visual distance and the visual angle per 1 mm illustrated inFIG. 11A.

On the other hand, FIG. 12 illustrates comparison of the luminance edgeimage (the luminance edge image data D22) when the above-described(dL*/mm)=0.5 was used as the luminance edge threshold and the luminanceedge image when the above-described (dL*/arcmin)=0.218 was used as theluminance edge threshold, for each appropriate visual distance (eachsize of the display screen) illustrated in FIG. 11A and FIG. 11B.Specifically, FIG. 12 illustrates comparison of difference of the edgeregion identified when the luminance edge region was defined with use ofthe luminance variation (the luminance edge intensity) per unit lengthon the display screen and when the luminance edge region was definedwith use of the luminance variation per unit visual angle.

It was found from the Example 2 illustrated in FIG. 11A, FIG. 11B, andFIG. 12 that the following effects are obtainable when the luminanceedge region is defined with use of the luminance variation per unitvisual angle (in the case where the luminance edge threshold(dL*/arcmin) is 0.218). Specifically, unlike the case where theluminance edge region is defined with use of the luminance variation perunit length on the display screen (in the case where the luminance edgethreshold (dL*/mm) is 0.5), it becomes possible to identify a certainluminance edge region irrespective of the size of the display screen(the appropriate visual distance of the observer). Accordingly, it ispossible to improve accuracy of the unevenness inspection.

Note that, in the Example 2, the difference of the edge region when theluminance edge region is identified has been illustrated. However, thesame applies to the difference of the edge region when the chroma edgeregion is identified. In other words, when the chroma edge region isdefined with use of the chroma variation per unit visual angle, it ispossible to identify a certain chroma edge region irrespective of thesize of the display screen (the appropriate visual angle of theobserver), unlike the case where the chroma edger region is defined withuse of the chroma variation per unit length on the display screen.

Example 3

FIG. 13 illustrates comparison of the chroma edge image (the chroma edgeimage data D12) and the binarized color-unevenness images (the binarizedcolor-unevenness image data D13) according to a comparative example andExample 3. Moreover, FIG. 14 illustrates comparison of the luminanceedge image (the luminance edge image data D22) and the binarizedluminance-unevenness image (the binarized luminance-unevenness imagedata D23) according to the comparative example and the Example 3.

Here, the Example 3 corresponded to an example in a case where theabove-described filter processing was performed after the imageseparation processing in the present embodiment was performed, and thecomparative example corresponded to an example in a case where theabove-described filter processing was performed without performing theimage separation processing in the present embodiment. Further, an imagewithout color-unevenness was used as the inspection target in FIG. 13,whereas an image without luminance-unevenness was used as the inspectiontarget in FIG. 14.

In the example illustrated in FIG. 13, although the inspection targetwas the image without color-unevenness, the above-described falsecolor-unevenness component occurred in both of the chroma edge image andthe binarized color-unevenness image in the comparative example, (thedetermination result: cross). Therefore, in the comparative example, itwas difficult to perform accurate color-unevenness inspection. Incontrast, in the Example 3, occurrence of such false color-unevennesscomponent was avoided (the determination result: circle). In otherwords, performing the above-described filter processing after the imageseparation processing in the present embodiment makes it possible toperform more accurate color-unevenness inspection without includingfalse color-unevenness information in the color space conversion.

Moreover, in the example illustrated in FIG. 14, although the inspectiontarget was the image without luminance-unevenness, the above-describedfalse luminance-unevenness component occurred in the binarizedluminance-unevenness image in the comparative example (the determinationresult: cross). Therefore, it was difficult to perform accurateluminance-unevenness inspection in the comparative example. In contrast,in the Example 3, occurrence of such false luminance-unevennesscomponent was avoided (the determination result: circle). In otherwords, performing the above-described filter processing after the imageseparation processing in the present embodiment makes it possible toperform more accurate luminance-unevenness inspection without includingthe false luminance-unevenness information in the color spaceconversion.

As described above, in the present embodiment, at the time ofcalculating the integrated evaluation value E with use of both of thecolor-unevenness inspection images (the various kinds of image data D11to D13) and the luminance-unevenness inspection images (the variouskinds of image data D21 to D23), the unevenness visibility with respectto both of color and luminance is considered. Accordingly, it ispossible to realize objective unevenness inspection further matched withhuman sense (integrated unevenness inspection including thecolor-unevenness inspection and the luminance-unevenness inspection).Moreover, at the time of generating such color-unevenness inspectionimages and such luminance-unevenness inspection images, the filterprocessing taking account of the visual spatial frequencycharacteristics is performed after the image separation processing forseparating the color component and the luminance component is performed.Therefore, occurrence of the false color-unevenness component and thefalse luminance-unevenness component is avoided, which makes it possibleto realize more accurate unevenness inspection. Consequently, it becomespossible to perform appropriate unevenness inspection.

Further, at the time of generating the color-unevenness inspectionimages, the chroma C is calculated while performing the correctionprocessing (the gain correction processing to a*) taking account ofdifference of the color-unevenness visibility depending on colors foreach of the image pickup pixels of the picked-up image. Therefore, it ispossible to realize the objective unevenness inspection further matchedwith human sense, and to perform further appropriate unevennessinspection.

Further, since the objective unevenness inspection further matched withhuman sense is realized, it is possible to improve efficiency indevelopment and design by using the subjective unevenness inspection inquality assessment in development and design.

Moreover, introducing the unevenness inspection in the presentembodiment in, for example, inspection process for mass production makesit possible to perform unevenness inspection stably and rapidly. Thismakes it possible to improve efficiency of assessment process and toimprove stability of quality of the product.

In addition, since the edge region (the luminance edge region and thechroma edge region) is defined with use of variation (the luminancevariation and the chroma variation) per unit visual angle, it becomespossible to identify minute edge region on the display screen asdescribed below. Specifically, first, for example, as illustrated inFIG. 15, for example, when the edge region is identified with use ofluminance difference, chroma difference, or the like between the pixelsdistanced by a pitch corresponding to a visual angle of 0.1 [rad] orlarger, the minute edge region is not identified, for example, in thelarge screen size of 40 [inches] or 80 [inches]. This is because, asillustrated in FIG. 15, the pitch on the display screen corresponding tothe visual angle of 0.1 [rad] becomes several hundred [mm] In contrast,in the case where the edge region is defined with use of the variationper unit visual angle, for example, when the unit visual angle isassumed to be 1 [′] as illustrated in FIG. 15, the pitch on the displayscreen corresponding to the unit visual angle may be suppressed to besmaller than 1 [mm] even if the size of the display screen is large.Accordingly, for example, in consideration of the case where the size ofthe display screen is large or the case where the high definition mobiledisplay is observed from an appropriate visual distance, it is possibleto identify the minute edge region. This makes it possible to improveaccuracy in the unevenness inspection.

Note that the edge region may be defined by varying the threshold (theedge threshold) of the variation (the luminance variation and the chromavariation) per unit length on the display screen based on the visualdistance, instead of defining the edge region with use of the variationper unit visual angle as described above. Specifically, for example, theluminance edge threshold and the chroma edge threshold may be definedwith use of the following expressions (14) and (15). Incidentally, inthese expressions, D represents the visual distance [mm], Lth (=0.5)represents the luminance edge threshold per unit length when the visualdistance D is 1500 [mm], and Cth (=2.0) represents the chroma edgethreshold per unit length when the visual distance D is 1500 [mm] Inthis way, also when the edge region is defined by varying the edgethreshold per unit length based on the visual distance, it is possibleto identify the minute edge region and to improve accuracy in theunevenness inspection, similarly to the case where the edge region isdefined with use of the variation per unit visual angle.Luminance Edge Threshold: (dL*/dx)=Lth×(1500/D)  (14)Chroma Edge Threshold: (dC*/dx)=Cth×(1500/D)  (15)

<Modifications>

Subsequently, modifications (modifications 1 and 2) of theabove-described embodiment are described. The modifications 1 and 2correspond to examples (clouded configuration examples) in which atleast a part of the functions of the image processing section 2described in the embodiment (the functions of the image generationsection 21, the parameter calculation section 22, and the inspectionprocessing section 22) is provided in a server, and networkcommunication is performed. Note that like numerals are used todesignate substantially like components in the embodiment, and thedescription thereof is appropriately omitted.

[Modification 1]

(Configuration)

FIG. 16 schematically illustrates an outline configuration example of anunevenness inspection system (an unevenness inspection system 1A)according to the modification 1, together with inspection targets 4A to4D. The unevenness inspection system 1A in the present modificationincludes a server 2A, a plurality of pairs of (four pairs in thisexample) image pickup apparatuses (image pickup sections) 3A to 3D andcontrol apparatuses 5A to 5D and a management apparatus (a managementsection) 6 that are connected with the server 2A through wired orwireless network NW.

Further, the four pairs of image pickup apparatuses 3A to 3D and controlapparatuses 5A to 5D are disposed for each of steps (manufacturingsteps) A to D in manufacture of the inspection targets 4A to 4D, and theunevenness inspection described in the above-described embodiment isperformed individually in each of the steps A to D as described later.Here, the step A corresponds to a step of manufacturing the inspectiontarget 4A (a backlight), and the step B corresponds to a step ofmanufacturing the inspection target 4B (a liquid crystal panel). Also,the step C corresponds to a step of manufacturing the inspection target4C (a panel module), and the step D corresponds to a step ofmanufacturing the inspection target 4D (a liquid crystal display unitthat is a final product).

Incidentally, the unevenness inspection method and the unevennessinspection program according to the present modification are embodied inthe unevenness inspection system 1A of the present modification.Therefore, the unevenness inspection method and the unevennessinspection program are described together.

The server 2A includes the image generation section 21, the parametercalculation section 22, and the inspection processing section 23. Inother words, the image generation section 21, the parameter calculationsection 22, and the inspection processing section 23 are all provided inthe server 2A. For example, the server 2A may have a function ofidentifying individual identification symbols of the control apparatuses5A to 5D, individual identification symbols of the image pickupapparatuses 3A to 3D, or an individual identification symbol of a user.Moreover, for example, the server 2A may have a function of performingstorage of transferred picked-up image data Din and performing theunevenness inspection processing to store the inspection result dataDout in itself or to transfer the inspection result data Dout to thecontrol apparatuses 5A to 5D. Such a server 2A may be configured of, forexample, an image processing data storage apparatus that is clustered athigh speed on a large scale.

The control apparatuses 5A to 5D are each connected to the server 2Athrough the above-described network NW, and have functions ofcontrolling operation of the image pickup apparatus 3A to 3D,respectively, controlling data transmission and reception, anddisplaying the unevenness inspection result. Specifically, the controlapparatuses 5A to 5D may transmit the picked-up image data Din obtainedfrom the image pickup apparatuses 3A to 3D, respectively, to the server2A through the network NW, and may receive the inspection result dataDout that is supplied from the server 2A to display the inspectionresult data Dout on its display section. In addition, at this time, forexample, the control apparatuses 5A to 5D may each compress and transferthe picked-up image data Din according to the needs of equipment forcircuit capacity of the network NW and the like. In this way, in thepresent modification, the image pickup apparatuses 3A to 3D arenetwork-connected to the server 2A indirectly (through the controlapparatuses 5A to 5D, respectively). Note that the control apparatuses5A to 5D may be each configured using, for example, a personal computer(PC).

The management apparatus 6 has a function of collectively managingresults of the unevenness inspection (the inspection result data Dout)performed at each of the above-described steps 4A to 4D, as will bedescribed later.

(Action and Effects)

Here, for example, when the unevenness inspection is performedindividually at each of the steps A to D with use of the imageprocessing apparatus 2 that is configured of a PC or the like describedin the above-described embodiment, the following issues may occur.

First, the first issue is as follows. Specifically, for example, whenthe image pickup apparatus 3A used at the step A is of XYZ filter typeand the number of data points is one million pixels, data stream of onemillion dots is obtained as three pieces of picked-up image data Din.This corresponds to data amount of about 40 MB as a normal text file.When the series of unevenness inspection processing described in theabove-described embodiment is performed based on such picked-up imagedata Din, the data amount doubles only by the image separationprocessing, and a memory amount secured for working at one stepincreases about 10 times.

Further, even if the color-unevenness inspection images (the variouskinds of image data D11 to D13) and the luminance-unevenness inspectionimages (the various kinds of image data D21 to D23) are stored as bitmap data for latter management, it is necessary to store six pieces ofone million pixel data at one inspection step, and it is thus necessaryto secure the memory amount of about 6 MB. In addition, if these imagesare stored as numerical sequence data, it is necessary to secure thememory amount of about 100 MB. In this way, when management maintainingtraceability is performed, it is necessary to secure a storage regionclose to 150 MB at one step for one inspection target and a workingmemory sufficient for successive data development of 400 MB. Forexample, when one million display units are manufactured in one year, amanufacturing tact for one unit becomes about 30 seconds, and thusstorage capacity of about 430 GB is necessary per day.

Performing processing and storage of such vast amount of data at eachstep demands a high cost, and the data amount is further increased asthe step proceeds to the step B, the step C, and the step D. Therefore,it is not easy to secure traceability even when defect determinationoccurs in the inspection.

Moreover, the second issue is as follows. Specifically, for example, inthe recent method of manufacturing the display unit, manufacturing thefinal product at one place is rare. At present, the manufacturing plantis changed for each assembly, and further, cross-border transportationis performed. However, inspection system in shipping inspection andarrival inspection in the middle may not be adequate, and it isdifficult to perform management of occurrence of defect such asunevenness in practice.

Accordingly, in the unevenness inspection system 1A in the presentmodification, as described above, the functions of the image processingapparatus 2 (the image generation section 21, the parameter calculationsection 22, and the inspection processing section 22) are provided inthe server 2A, and the image pickup apparatuses 3A to 3D and the controlapparatuses 5A to 5D provided at the steps A to D, respectively, arenetwork-connected with the server 2A. As a result, the expensive imageprocessing apparatus 2 capable of performing high-speed computing may bereplaced with an inexpensive PC or the like having a function of, forexample, data compression and data transfer, which may result inreduction in production cost. In other words, at each of the steps A toD, acquisition of the picked-up image Din by the image pickup apparatus3A to 3D and display of the inspection result data Dout are onlyperformed, which reduces the manufacturing cost and avoids oppression ofmanufacturing tact by the inspection. Consequently, it becomes possibleto realize more appropriate unevenness inspection at high speed with lowcost, and to perform quality management easily.

Moreover, if the inspection information at each of the steps A to D areuniformly managed, traceability of defect determined at the inspectionstep is secured, which makes it possible to promptly performinvestigation of the cause of the defect and development ofcountermeasures. For example, if defect occurs in the inspection at thestep D, it becomes possible to determine whether the defect is caused byaccumulated factors or the defect suddenly occurs only at the step D byreference to connected data at other steps. Accordingly, it becomespossible to rapidly determine the cause of the defect, and to minimizemanufacturing defects. Moreover, when the defect is caused byaccumulated factors, it is possible to promptly provide feedback to thedesign. This makes it possible to prevent accumulation of defects.Further, performing the same inspection at shipping and arriving makesit easy to found defect by transportation. Thus, entire qualitymanagement becomes easy. In addition, since the operation forcalculating the evaluation results are uniformly managed, it is possibleto prevent operational mistake, intentional falsification, etc. at eachstep, which makes it easy to perform quality management. In this way,the quality management becomes easy, and quality preservation of theproduct to be provided to general consumers, furthermore, qualityimprovement and inventory management adjustment are also performedeasily.

Further, for example, when a server manager on the network NW charges auser for management instead of reducible cost of the image processingunit 2 and performs management, it becomes possible for the user toreceive the benefits of computing at a constant high-speed andmanagement and storage of the data. Accordingly, when the inspection atshipping and arriving is performed utilizing the inexpensive inspectioncost, it becomes possible to manage the defects and the like occurred intransportation. Note that examples of the charging method at this timemay include charging to a user having a user identification symbol, forexample, depending on the size of the image, the size of the image data,process time, or a combination thereof.

Note that, in the present modification, the case where the imagegeneration section 21, the parameter calculation section 22, and theinspection processing section 23 are all provided in the server 2A hasbeen described as an example, however the configuration is not limitedthereto. Alternatively, one or more of the image generation section 21,the parameter calculation section 22, and the inspection processingsection 23 may be provided in the single server 2A. Also, the unevennessinspection system 1A may include not the plurality of image pickupapparatuses (image pickup sections) but only one image pickup apparatus.

[Modification 2]

FIG. 17 schematically illustrates an outline configuration example of anunevenness inspection system (an unevenness inspection system 1B)according to the modification 2, together with the inspection targets 4Ato 4D. The unevenness inspection system 1B of the present modificationincludes the server 2A, and the plurality of (four in this example)image pickup apparatuses 3A to 3D and the management apparatus 6 thatare connected with the server 2A through the wired or wireless networkNW. In other words, the unevenness inspection system 1B corresponds to asystem configured by omitting (not providing) the control apparatuses 5Ato 5D at the respective steps A to D from the unevenness inspectionsystem 1A of the modification 1, and other configurations of theunevenness inspection system 1B are basically similar to those of theunevenness inspection system 1A of the modification 1. Note that theunevenness inspection method and the unevenness inspection programaccording to the present modification are embodied in the unevennessinspection system 1A of the present modification. Thus, the unevennessinspection method and the unevenness inspection program according to thepresent modification will be described together below.

In the present modification, each of the image pickup apparatuses 3A to3D is connected to the serer 2A through the network NW, and has afunction of performing data transmission and data reception.Specifically, each of the image pickup apparatuses 3A to 3D may transmitthe obtained picked-up image data Din to the server 2A through thenetwork NW, and may receive the inspection result data Dout suppliedfrom the server 2A. Further, at this time, for example, each of theimage pickup apparatuses 3A to 3D may compress and transfer thepicked-up image data Din according to the needs of equipment for circuitcapacity of the network NW and the like. In this way, in the presentmodification, each of the image pickup apparatuses 3A to 3D isnetwork-connected with the server 2A directly (without the controlapparatuses 5A to 5D).

Also in the present modification having such a configuration, it ispossible to basically obtain effects similar to those of theabove-described modification 1 by the similar functions.

Moreover, in the present modification in particular, the controlapparatuses 5A to 5D are unnecessary. Therefore, it is possible tofurther reduce cost at each of the steps.

Incidentally, also in the present modification, one or more of the imagegeneration section 21, the parameter calculation section 22, and theinspection processing section 22 may be provided in the single server2A. Moreover, the unevenness inspection system 1B may include not theplurality of image pickup apparatuses (image pickup sections) but onlyone image pickup apparatus.

<Other Modifications>

Hereinbefore, although the technology of the disclosure has beendescribed with referring to the embodiment and the modifications, thetechnology is not limited to the embodiment and the like, and variousmodifications may be made.

For example, in the above-described embodiment and the like, the casewhere the three parameters of the chroma edge area ratio Sce, thecolor-unevenness area ratio Sc, and the maximum chroma Cmax are used asthe color-unevenness evaluation value Ec has been described. However,other parameters may be used in addition thereto (or in place thereof).In addition, one or more of the three parameters may be used as thecolor-unevenness evaluation value Ec. Incidentally, out of the threeparameters, in particular, at least two parameters of the chroma edgearea ratio Sce and the color-unevenness area ratio Sc may be desirablyused. This is because these two parameters contribute to thecolor-unevenness evaluation value Ec relatively largely since humanbeing tends to give weight to spatial extent in determination of thedegree of color-unevenness.

Also, in the above-described embodiment and the like, the case where thethree parameters of the luminance edge area ratio Sle, theluminance-unevenness area ratio Sl, and the maximum luminance differenceΔL*max are used as the luminance-unevenness evaluation value El has beendescribed. However, other parameters may be used in addition thereto (orin place thereof). In addition, one or more of the three parameters maybe used as the luminance-unevenness evaluation value El. Incidentally,out of the three parameters, in particular, at least two parameters ofthe luminance-edge area ratio Sle and the luminance-unevenness arearatio Sl may be desirably used. This is because these two parameterscontribute to the luminance-unevenness evaluation value El relativelylargely since human being tends to give weight to spatial extent indetermination of the degree of luminance-unevenness.

Further, in the above-described embodiment and the like, the examples ofthe color-unevenness inspection image and the luminance-unevennessinspection image have been specifically described. However, thecolor-unevenness inspection image and the luminance-unevennessinspection image are not limited to those described in theabove-described embodiment.

In addition, in the above-described embodiment and the like, the casewhere the chroma C is calculated while the correction processing (thegain correction processing) taking account of difference of thecolor-unevenness visibility depending on colors is performed ingeneration of the color-unevenness inspection images has been described.However, such gain correction processing may not be performed dependingon a case.

Moreover, in the above-described embodiment and the like, the case wherethe inspection target of the unevenness inspection is the display screenof the display unit performing color picture display has been described.However, the inspection target of the technology may be other than thedisplay unit (for example, an illumination unit (such as a backlight)).

Furthermore, in the above-described embodiment and the like, the casewhere the image pickup apparatus and the image processing apparatus areseparated from each other in the unevenness inspection system has beendescribed. However, these apparatuses may be provided in the sameapparatus.

In addition, the series of processes (the respective functions of theimage generation section, the calculation section, the inspectionsection, the management section, and the like) described in theabove-described embodiment and the like may be executed by hardware(circuits) or may be executed by software (programs). In the case wherethe series of processes is executed by software, the software isconfigured of a program group causing a computer (PC, a microcomputer inthe server, or the like) to execute each of the above-describedfunctions. For example, each program may be incorporated in theabove-described computer in advance or may be installed from any networkor a recording medium to the above-described computer and used.

Note that the technology may be configured as follows.

[1] An unevenness inspection system including:

an image pickup section configured to acquire a picked-up image of aninspection target;

an image generation section configured to generate a color-unevennessinspection image and a luminance-unevenness inspection image, based onthe picked-up image;

a calculation section configured to use both of the color-unevennessinspection image and the luminance-unevenness inspection image tocalculate an evaluation parameter; and

an inspection section configured to use the calculated evaluationparameter to perform unevenness inspection, wherein

the image generation section performs image separation processing toseparate a color component and a luminance component on the picked-upimage, to generate a color-component image and a luminance-componentimage, and individually performs filter processing taking account ofvisual spatial frequency characteristics on the color-component imageand the luminance-component image to respectively generate thecolor-unevenness inspection image and the luminance-unevennessinspection image, based on the filter-processed color-component imageand the filter-processed luminance-component image, and

the calculation section calculates the evaluation parameter inconsideration of unevenness visibility with respect to both of color andluminance.

[2] The unevenness inspection system according to [1], wherein

the calculation section uses the color-unevenness inspection image tocalculate a color-unevenness evaluation parameter and uses theluminance-unevenness inspection image to calculate aluminance-unevenness evaluation parameter, and weights and adds thecolor-unevenness evaluation parameter and the luminance-unevennessevaluation parameter to calculate an integrated evaluation parameter asthe evaluation parameter.

[3] The unevenness inspection system according to [2], wherein

the integrated evaluation parameter E is represented by followingexpression (1), and weighting coefficients A and B are determined inconsideration of the unevenness visibility,E=A×Ec+B×El  (1)where Ec represents the color-unevenness evaluation parameter, Elrepresents the luminance-unevenness evaluation parameter, and A and Brepresent the weighting coefficients.

[4] The unevenness inspection system according to [2] or [3], wherein

the inspection section determines that a degree of unevenness in theinspection target is large, based on increase of the integratedevaluation parameter, and determines that the degree of unevenness inthe inspection target is small, based on decrease of the integratedevaluation parameter.

[5] The unevenness inspection system according to any one of [1] to [4],wherein

the image generation section performs correction processing takingaccount of difference of color-unevenness visibility depending oncolors, on the filter-processed color-component image, and thengenerates the color-unevenness inspection image.

[6] The unevenness inspection system according to [5], wherein

the image generation section calculates chroma after performing thecorrection processing, in each unit region of the filter-processedcolor-component image, and uses the calculated chroma to generate thecolor-unevenness inspection image.

[7] The unevenness inspection system according to [6], wherein

the image generation section calculates values (a*, b*) in CIELAB colorspace in each unit region of the filter-processed color-component image,and performs gain correction processing that is represented by followingexpression (2) as the correction processing, on the calculated value a*,and then calculates chroma C with use of following expression (3),a*′=(α×a*)(for a*>0:gain α>1,for a*≤0:gain α=1)  (2)C={(a*′)²+(b*)²}^(1/2)  (3)

[8] The unevenness inspection system according to any one of [2] to [7],wherein

as the color-unevenness evaluation parameter, at least a chroma edgearea ratio that is an area ratio of a chroma edge region to entireregion of the inspection target and a color-unevenness area ratio thatis an area ratio of a color-unevenness region to the entire region ofthe inspection target are used.

[9] The unevenness inspection system according to [8], wherein

as the color-unevenness evaluation parameter, the chroma edge arearatio, the color-unevenness area ratio, and a maximum chroma in theentire color-unevenness region are used.

[10] The unevenness inspection system according to [9], wherein

the calculation section uses the color-unevenness inspection image tocalculate the chroma edge area ratio, the color-unevenness area ratio,and the maximum chroma, and weights and adds the chroma edge area ratio,the color-unevenness area ratio, and the maximum chroma to calculate thecolor-unevenness evaluation parameter.

[11] The unevenness inspection system according to any one of [2] to[10], wherein

as the luminance-unevenness evaluation parameter, at least a luminanceedge area ratio that is an area ratio of a luminance edge region toentire region of the inspection target and a luminance-unevenness arearatio that is an area ratio of a luminance-unevenness region to theentire region of the inspection target are used.

[12] The unevenness inspection system according to [11], wherein

as the luminance-unevenness evaluation parameter, the luminance edgearea ratio, the luminance-unevenness area ratio, and a maximum luminancedifference are used, the maximum luminance difference being a maximumvalue of an absolute value of a difference between luminance in theentire luminance-unevenness region and average luminance of a whiteimage.

[13] The unevenness inspection system according to [12], wherein

the calculation section uses the luminance-unevenness inspection imageto calculate the luminance edge area ratio, the luminance-unevennessarea ratio, and the maximum luminance difference, and weights and addsthe luminance edge area ratio, the luminance-unevenness area ratio, andthe maximum luminance difference to calculate the luminance-unevennessevaluation parameter.

[14] The unevenness inspection system according to any one of [1] to[13], wherein

one or more of the image generation section, the calculation section,and the inspection section are provided in a single server, and

the image pickup section is provided in each of one or a plurality ofimage pickup apparatuses that are directly or indirectly connected tothe server through network.

[15] The unevenness inspection system according to [14], wherein

the image pickup apparatus is provided for every plurality of steps inmanufacturing the inspection target, and

unevenness inspection by the inspection section is performedindividually for every plurality of steps.

[16] The unevenness inspection system according to [15], furtherincluding

a management section connected to the network and configured tocollectively manage results of the unevenness inspection performed forevery plurality of steps.

[17] The unevenness inspection system according to any one of [14] to[16], wherein

the image generation section, the calculation section, and theinspection section are provided in the server, and the image pickupsection is provided in each of the one or the plurality of image pickupapparatuses, and

the picked-up image is supplied from the image pickup apparatus to theserver through the network, and result data of the unevenness inspectionis supplied from the server to the image pickup apparatus through thenetwork.

[18] The unevenness inspection system according to any one of [1] to[17], wherein

the inspection target is a display screen of a display unit performingcolor picture display.

[19] An unevenness inspection method including:

a step of acquiring a picked-up image of an inspection target;

a generation step of generating a color-unevenness inspection image anda luminance-unevenness inspection image, based on the picked-up image;

a calculation step of using both of the color-unevenness inspectionimage and the luminance-unevenness inspection image to calculate anevaluation parameter; and

an inspection step of using the calculated evaluation parameter toperform unevenness inspection, wherein

in the generation step, image separation processing to separate a colorcomponent and a luminance component is performed on the picked-up imageto generate a color-component image and a luminance-component image, andfilter processing taking account of visual spatial frequencycharacteristics is individually performed on the color-component imageand the luminance-component image to respectively generate thecolor-unevenness inspection image and the luminance-unevennessinspection image, based on the filter-processed color-component imageand the filter-processed unevenness-component image, and

in the calculation step, the evaluation parameter is calculated inconsideration of unevenness visibility with respect to both of color andluminance.

[20] An unevenness inspection program causing a computer to execute:

a step of acquiring a picked-up image of an inspection target;

a generation step of generating a color-unevenness inspection image anda luminance-unevenness inspection image, based on the picked-up image;

a calculation step of using both of the color-unevenness inspectionimage and the luminance-unevenness inspection image to calculate anevaluation parameter; and

an inspection step of using the calculated evaluation parameter toperform unevenness inspection, wherein

in the generation step, image separation processing to separate a colorcomponent and a luminance component is performed on the picked-up imageto generate a color-component image and a luminance-component image, andfilter processing taking account of visual spatial frequencycharacteristics is individually performed on the color-component imageand the luminance-component image to respectively generate thecolor-unevenness inspection image and the luminance-unevennessinspection image, based on the filter-processed color-component imageand the filter-processed luminance-component image, and

in the calculation step, the evaluation parameter is calculated inconsideration of unevenness visibility with respect to both of color andluminance.

This application is based upon and claims the benefit of priority of theJapanese Patent Application No. 2013-41573 filed in the Japan PatentOffice on Mar. 4, 2013, the entire contents of which are incorporatedherein by reference.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations, and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

What is claimed is:
 1. An unevenness inspection system comprising:circuitry configured to: acquire a picked-up image of an inspectiontarget; separate a color component and a luminance component on thepicked-up image; generate a color-component image and aluminance-component image based on the separated color component andluminance component; individually filter process taking account ofvisual spatial frequency characteristics on the color-component imageand the luminance-component image; generate a color-unevennessinspection image and a luminance-unevenness inspection image, based onthe filter-processed color-component image and the filter-processedluminance-component image; calculate, based on the color-unevennessinspection image, at least a color-unevenness area ratio that is an arearatio of a color-unevenness region to the entire region of theinspection target as a color-unevenness evaluation parameter; calculatea luminance-unevenness evaluation parameter based on theluminance-unevenness inspection image; integrate the color-unevennessevaluation parameter and the luminance-unevenness evaluation parametertogether to generate an evaluation parameter; and use the evaluationparameter to perform unevenness inspection in a manufacturing process ofthe inspection target independently from a capability of an inspector.2. The unevenness inspection system according to claim 1, wherein thecircuitry is further configured to use the color-unevenness inspectionimage to calculate a color-unevenness evaluation parameter and use theluminance-unevenness inspection image to calculate aluminance-unevenness evaluation parameter, and weights and add thecolor-unevenness evaluation parameter and the luminance-unevennessevaluation parameter to calculate an integrated evaluation parameter Eas the evaluation parameter.
 3. The unevenness inspection systemaccording to claim 2, wherein the integrated evaluation parameter E isrepresented by following expression (1), and weighting coefficients Aand B are determined in consideration of an unevenness visibility,E=A×Ec+B×El  (1) where Ec represents the color-unevenness evaluationparameter, El represents the luminance-unevenness evaluation parameter,and A and B represent the weighting coefficients.
 4. The unevennessinspection system according to claim 2, wherein the circuitry is furtherconfigured to determine that a degree of unevenness in the inspectiontarget is large, based on increase of the integrated evaluationparameter, and determine that the degree of unevenness in the inspectiontarget is small, based on decrease of the integrated evaluationparameter.
 5. The unevenness inspection system according to claim 1,wherein the circuitry is further configured to perform correctionprocessing taking account of difference of color-unevenness visibilitydepending on colors, on the filter-processed color-component image, andthen generate the color-unevenness inspection image.
 6. The unevennessinspection system according to claim 5, wherein the circuitry is furtherconfigured to calculate chroma after performing the correctionprocessing, in each unit region of the filter-processed color-componentimage, and use the calculated chroma to generate the color-unevennessinspection image.
 7. The unevenness inspection system according to claim6, wherein the circuitry is further configured to calculate values (a*,b*) in CIELAB color space in each unit region of the filter-processedcolor-component image, and perform gain correction processing that isrepresented by following expression (2) as the correction processing, onthe calculated value a*, and then calculates chroma C with use offollowing expression (3),a*′=(α×a*)(for a*>0:gain α>1,[or a*≤0:gain α=1)  (2)C={(a*′)2+(b*)2}^(½)  (3)
 8. The unevenness inspection system accordingto claim 2, wherein as the color-unevenness evaluation parameter, atleast a chroma edge area ratio that is an area ratio of a chroma edgeregion to entire region of the inspection target and thecolor-unevenness area ratio that is the area ratio of thecolor-unevenness region to the entire region of the inspection targetare used.
 9. The unevenness inspection system according to claim 8,wherein as the color-unevenness evaluation parameter, the chroma edgearea ratio, the color-unevenness area ratio, and a maximum chroma in theentire color-unevenness region are used.
 10. The unevenness inspectionsystem according to claim 9, wherein the circuitry is further configuredto use the color-unevenness inspection image to calculate the chromaedge area ratio, the color-unevenness area ratio, and the maximumchroma, and weights and add the chroma edge area ratio, thecolor-unevenness area ratio, and the maximum chroma to calculate thecolor-unevenness evaluation parameter.
 11. The unevenness inspectionsystem according to claim 2, wherein as the luminance-unevennessevaluation parameter, at least a luminance edge area ratio that is anarea ratio of a luminance edge region to entire region of the inspectiontarget and a luminance-unevenness area ratio that is an area ratio of aluminance-unevenness region to the entire region of the inspectiontarget are used.
 12. The unevenness inspection system according to claim11, wherein as the luminance-unevenness evaluation parameter, theluminance edge area ratio, the luminance-unevenness area ratio, and amaximum luminance difference are used, the maximum luminance differencebeing a maximum value of an absolute value of a difference betweenluminance in the entire luminance-unevenness region and averageluminance of a white image.
 13. The unevenness inspection systemaccording to claim 12, wherein the circuitry is further configured touse the luminance-unevenness inspection image to calculate the luminanceedge area ratio, the luminance-unevenness area ratio, and the maximumluminance difference, and weights and add the luminance edge area ratio,the luminance-unevenness area ratio, and the maximum luminancedifference to calculate the luminance-unevenness evaluation parameter.14. The unevenness inspection system according to claim 1, wherein afirst circuit of the circuitry is provided in a single server, and asecond circuit of the circuitry is provided in each of one or aplurality of image pickup apparatuses that are directly or indirectlyconnected to the server through network.
 15. The unevenness inspectionsystem according to claim 14, wherein an image pickup apparatus isprovided for every plurality of steps in manufacturing the inspectiontarget, and unevenness inspection is performed individually for everyplurality of steps.
 16. The unevenness inspection system according toclaim 15, wherein the circuitry is further configured to collectivelymanage results of the unevenness inspection performed for everyplurality of steps.
 17. The unevenness inspection system according toclaim 14, wherein the first circuit is provided in the server, and thesecond circuit is provided in each of the one or the plurality of imagepickup apparatuses, and the picked-up image is supplied from the imagepickup apparatus to the server through the network, and result data ofthe unevenness inspection is supplied from the server to the imagepickup apparatus through the network.
 18. The unevenness inspectionsystem according to claim 1, wherein the inspection target is a displayscreen of a display performing color picture display.
 19. An unevennessinspection method comprising: acquiring a picked-up image of aninspection target; separating a color component and a luminancecomponent on the picked-up image; generating a color-component image anda luminance-component image based on the separated color component andluminance component; individually filter processing taking account ofvisual spatial frequency characteristics on the color-component imageand the luminance-component image; generating a color-unevennessinspection image and a luminance-unevenness inspection image, based onthe filter-processed color-component image and the filter-processedluminance-component image; calculating, based on the color-unevennessinspection image, at least a color-unevenness area ratio that is an arearatio of a color-unevenness region to the entire region of theinspection target as a color-unevenness evaluation parameter;calculating a luminance-unevenness evaluation parameter based on theluminance-unevenness inspection image; integrating the color-unevennessevaluation parameter and the luminance-unevenness evaluation parametertogether to generate an evaluation parameter; and using the evaluationparameter to perform unevenness inspection in a manufacturing process ofthe inspection target independently from a capability of an inspector.20. A non-transitory computer readable medium storing an unevennessinspection program thereon that, when executed by a computer, causes thecomputer to perform a method comprising: acquiring a picked-up image ofan inspection target; separating a color component and a luminancecomponent on the picked-up image; generating a color-component image anda luminance-component image based on the separated color component andluminance component; individually filter processing taking account ofvisual spatial frequency characteristics on the color-component imageand the luminance-component image; generating a color-unevennessinspection image and a luminance-unevenness inspection image, based onthe filter-processed color-component image and the filter-processedluminance-component image; calculating, based on the color-unevennessinspection image, at least a color-unevenness area ratio that is an arearatio of a color-unevenness region to the entire region of theinspection target as a color-unevenness evaluation parameter;calculating a luminance-unevenness evaluation parameter based on theluminance-unevenness inspection image; integrating the color-unevennessevaluation parameter and the luminance-unevenness evaluation parametertogether to generate an evaluation parameter; and using the evaluationparameter to perform unevenness inspection in a manufacturing process ofthe inspection target independently from a capability of an inspector.21. An unevenness inspection system comprising: circuitry configured to:acquire a picked-up image of an inspection target; convert a signal ofthe picked-up image into a signal formed of tristimulus values Xi, Yi,and Zi; separate a color-component image and a luminance-component imageon the picked-up image by (i) removing luminance-distributioninformation from the signal (Xi, Yi, Zi) while maintaining colordistribution information to generate the color-component image formed ofa signal (XC, YC, ZC), XC and ZC being calculated by the followingexpressions: 500×[f(Xi/Xn)−f(Yi/Yn)]=500×[f(XC/Xn)−f(YC/Yn)] and200×[f(Yi/Yn)−f(Zi/Zn)]=200×[f(YC/Yn)−f(ZC/Zn)]; and (ii) removingcolor-distribution information from the signal (Xi, Yi, Zi) whilemaintaining luminance distribution information to generate theluminance-component image formed of a signal (XL, YL, ZL), XL and ZLbeing calculated by the following expressions: 500×[f(XL/Xn)−f(YL/Yn)]=0and 200×[f(YL/Yn)−f(ZL/ZN)]=0; individually filter process takingaccount of visual spatial frequency characteristics on thecolor-component image and the luminance-component image; generate acolor-unevenness inspection image and a luminance-unevenness inspectionimage, based on the filter-processed color-component image and thefilter-processed luminance-component image; calculate an evaluationparameter based on the color-unevenness inspection image and theluminance-unevenness inspection image; and use the evaluation parameterto perform unevenness inspection in a manufacturing process of theinspection target independently from a capability of an inspector.